GENESIS: Towards Explainable Causal Discovery

2026-08-04

AI Summary

This study addresses the challenges of structural ambiguity in causal discovery and the lack of edge-level interpretability in existing hybrid methods, which hinder independent validation in real-world applications. The authors propose the GENESIS framework, which formalizes “decision traceability” as a first-class objective in causal discovery. GENESIS decomposes graph construction into interpretable decision points based on three-node structural motifs—chains, forks, and colliders—and integrates statistical evidence, Markov blanket consistency tests, and domain knowledge elicited via large language models to enable semantic reasoning when empirical evidence is insufficient. The method consistently outperforms purely statistical approaches across all sample sizes in terms of Structural Hamming Distance (SHD), matches the performance of state-of-the-art LLM-augmented hybrid methods, and achieves fully auditable edge-wise decisions with 100% traceability.

Abstract

Causal Discovery (CD) from observational data faces two fundamental challenges. First, purely statistical methods often lack the power to resolve structural ambiguities in low-sample regimes. Second, although LLM-assisted hybrid approaches improve structure recovery through semantic reasoning, the influence of that reasoning on individual edge decisions remains largely opaque. Consequently, existing hybrid methods fail to satisfy a fundamental requirement: explaining why a particular edge is included or excluded in the learned directed acyclic graph (DAG). This is critical in real-world applications, where no ground-truth DAG exists and every structural decision must be independently justified. We formalize this requirement as decision traceability, requiring every inferred edge to be supported by auditable statistical evidence, Markov Blanket consistency, or explicit domain reasoning. We propose GENESIS, an explainable hybrid CD framework that decomposes graph construction into interpretable decision points. GENESIS first identifies and scores three-node structural motifs, including chains, forks, and colliders, to establish transparent structural priors, then progressively refines the graph by integrating these priors with observational evidence, invoking domain knowledge only when statistical evidence is insufficient. By design, every edge decision is resolved through an auditable source of evidence. Experiments show that GENESIS achieves 100% decision traceability across all settings, establishing explainability as a first-class objective in causal discovery. Despite this additional requirement, GENESIS consistently outperforms purely statistical CD methods on the majority of benchmark datasets across all sample regimes in terms of Structural Hamming Distance (SHD), while achieving performance comparable to state-of-the-art LLM-assisted approaches.

Problem

• Causal Discovery
• Explainability
• Decision Traceability
• Directed Acyclic Graph
• Structural Ambiguity

Innovation

• explainable causal discovery
• decision traceability
• structural motifs
• hybrid causal discovery
• Markov Blanket consistency